166 citations · 424 across the 44 of their papers we have counts for
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Copula-Based Normalizing Flows
Mike Laszkiewicz, Johannes Lederer, Asja Fischer
Normalizing flows, which learn a distribution by transforming the data to samples from a Gaussian base distribution, have proven powerful density approximations. But their expressi…
Regularization and Reparameterization Avoid Vanishing Gradients in Sigmoid-Type Networks
Leni Ven, Johannes Lederer
Deep learning requires several design choices, such as the nodes' activation functions and the widths, types, and arrangements of the layers. One consideration when making these ch…
Targeted Deep Learning: Framework, Methods, and Applications
Shih-Ting Huang, Johannes Lederer
Deep learning systems are typically designed to perform for a wide range of test inputs. For example, deep learning systems in autonomous cars are supposed to deal with traffic sit…
DeepMoM: Robust Deep Learning With Median-of-Means
Shih-Ting Huang, Johannes Lederer
Data used in deep learning is notoriously problematic. For example, data are usually combined from diverse sources, rarely cleaned and vetted thoroughly, and sometimes corrupted on…
Activation Functions in Artificial Neural Networks: A Systematic Overview
Johannes Lederer
Activation functions shape the outputs of artificial neurons and, therefore, are integral parts of neural networks in general and deep learning in particular. Some activation funct…